Rate Cutting Tax Reforms and Corporate Tax Competition in Europe

While there is a large and growing number of studies on the determinants of corporate tax rates, the literature has so far ignored the fact that the behavior of governments in setting tax rates is often best described as a discrete choice decision problem. We set up an empirical model that relates a government's decision whether to cut its corporate tax rate to the country's own inherited tax and taxes in neighboring countries. Using comprehensive data on corporate tax reforms in Europe since 1980, we find evidence suggesting that the position in terms of the tax burden imposed on corporate income relative to geographical neighbors strongly affects the probability of rate cutting tax reforms. Countries are particularly likely to cut their statutory tax rate if the inherited tax is high and if they are exposed to low-tax neighbors. --Tax reform,tax competition,corporate taxes

Goal-Directed Decision Making with Spiking Neurons.

UNLABELLED: Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT: Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level.This research was supported by the Swiss National Science Foundation (J.F., Grant PBBEP3 146112) and the Wellcome Trust (J.F. and M.L.).This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Society for Neuroscience

A statistical and machine learning approach to the study of astrochemistry

This thesis uses a variety of statistical and machine learning techniques to provide new insight into astrochemical processes. Astrochemistry is the study of chemistry in the universe. Due to the highly non-linear nature of a variety of competing factors, it is often difficult to understand the impact of any individual parameter on the abundance of molecules of interest. It is for this reason we present a number of techniques that provide insight. Chapter 2 is a chemical modelling study that considers the sensitivity of a glycine chemical network to the addition of two H2 addition reactions across a number of physical environments. This work considers the concept of a ``hydrogen economy" within the context of chemical reaction networks and demonstrates that H2 decreases the abundance of glycine, one of the simplest amino acids, as well as its precursors. Chapter 3 considers a methodology that involves utilising the topology of a chemical network in order to accelerate the Bayesian inference problem by reducing the dimensionality of the parameters to be inferred at once. We demonstrate that a network can be simplified as well as split into smaller pieces for the inference problem by using a toy network. Chapter 4 considers how the dimensionality can be simplified by exploiting the physics of the underlying chemical reaction mechanisms. We do this by realising that the most pertinent reaction rate parameter is the binding energy of the more mobile species. This significantly reduces the dimensionality of the problem we have to solve. Chapter 5 builds on the work done in Chapters 3 and 4. The MOPED algorithm is utilised to identify which species should be prioritised for detection in order to reduce the variance of our binding energy posterior distributions. Chapter 6 introduces the use of machine learning interpretability to provide better insights into the relationships between the physical input parameters of a chemical code and the final abundances of various species. By identifying the relative importance of various parameters and quantifying this, we make qualitative comparisons to observations and demonstrate good agreement. Chapter 7 uses the same methods as in Chapters 4, 5 and 6 in light of new JWST observations. The relationship between binding energies and the abundances of species is also explored using machine learning interpretability techniques

The Socio-Economic Impacts of the Coming of the Railways to Hertfordshire, Bedfordshire and Buckinghamshire 1838 - 1900

This research presents a demographic investigation into the effects the development of Britain’s railways in the Victorian Era had on the largely rural counties of Hertfordshire, Bedfordshire and Buckinghamshire. A ‘gateway’ to London, this region was traversed by many lines with a wide range of impacts. Railway historiography has questioned the extent to which railways affected national development; contemporary views of their central importance giving way to more critical opinion. Local rural studies have been recognised in addressing this; these at present are, however, few. Comparing and contrasting the three counties, the findings were used to create hypotheses of rural impacts, subsequently tested for accuracy and applicability by comparison with individual settlements. They demonstrated that occupations became decreasingly agricultural; railways having varying involvement. Sometimes a key factor, mostly they were of a supporting nature triggering knock-on effects. Land use became more urbanised but this was not railway originating; contrarily land use affected rail development itself. Railways, nonetheless, actively boosted urbanisation and industry by 1900, and in cases even supported agriculture. Population changes were assisted by railways, particularly rural-urban migration, but while aiding later in the period, railways did not initiate the process. A case study of Wolverton (Buckinghamshire), the first planned ‘railway town’, reveal exceptional differences even down to the appropriateness of the broader historiography. Limited prior research on this settlement type had been undertaken, and this study revealed their development was more complex than at first glance. As a result, a new structural framework was created to explain how they could transform from company tool to independent town. The contribution of this research is thus threefold. In analysing a new region, another area is added to a growing number collectively building a national understanding from a local level. As a rural region yet close to London, this shows that while current historiographical ‘facilitator’ views are correct, variation was rife. The hypotheses present a starting point for future rural rail studies – a method for comparing regions alongside a list of investigable aspects. Lastly, the proposed model for ‘railway town’ development provides a framework for comparison not just of these settlements but potentially other forms of planned ‘company town’. While railways were one factor among many, their importance should not be underestimated